检索结果-内蒙古大学图书馆

Experimental Evaluation of Covariates Effects on Periocular Biometrics: A Robust Security Assessment Framework

Journal of Data and Information Quality 2023年第2期15卷 1-25页

作者： Kumar, Gautam Bakshi, Sambit Sangaiah, Arun Kumar Sa, Pankaj Kumar National Institute of Technology Rourkela Sector II Odisha Rourkela769 008 India International Graduate Institute of Ai National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The growing integration of technology into our lives has resulted in unprecedented amounts of data that are being exchanged among devices in an Internet of Things (IoT) environment. Authentication, identification, and device heterogeneities are major security and privacy concerns in IoT. One of the most effective solutions to avoid unauthorized access to sensitive information is biometrics. Deep learning-based biometric systems have been proven to outperform traditional image processing and machine learning techniques. However, the image quality covariates associated with blur, resolution, illumination, and noise predominantly affect recognition performance. Therefore, assessing the robustness of the developed solution is another important concern that still needs to be investigated. This article proposes a periocular region-based biometric system and explores the effect of image quality covariates (artifacts) on the performance of periocular recognition. To simulate the real-time scenarios and understand the consequences of blur, resolution, and bit-depth of images on the recognition accuracy of periocular biometrics, we modeled out-of-focus blur, camera shake blur, low-resolution, and low bit-depth image acquisition using Gaussian function, linear motion, interpolation, and bit plan slicing, respectively. All the images of the UBIRIS.v1 database are degraded by varying strength of image quality covariates to obtain degraded versions of the database. Afterward, deep models are trained with each degraded version of the database. The performance of the model is evaluated by measuring statistical parameters calculated from a confusion matrix. Experimental results show that among all types of covariates, camera shake blur has less effect on the recognition performance, while out-of-focus blur significantly impacts it. Irrespective of image quality, the convolutional neural network produces excellent results, which proves the robustness of the developed model. © 2023 Co

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices

引用

Journal of Artificial Intelligence and Technology 2023年第4期3卷 162-172页

作者： M.Manimaran Murali Dhar Roger Norabuena-Figueroa R.Mahaveerakannan S.Saraswathi K.Selvakumarasamy Department of Computer Science and Engineering(AI&ML) School of ComputingMohan Babu UniversityTirupatiAndhra PradeshIndia Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology AvadiChennaiTNIndia Universidad Privada San Juan Bautista LimaPerú Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennaiTNIndia

Smart homes present a serious challenge for the aged and those with mobility issues due to the environment’s inherent *** people have the propensity to fall over when bending over in these ***,they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for(CM-I and CM-II).The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization *** compared actual and simulated data involving missteps,mishaps,and crashes to gauge the programmers’*** suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future *** CM-II model had the highest prediction accuracy of any model identified in the literature,at 0.98 when compared to the test *** the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers,the number of hacks aimed at the IoT has grown *** is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the *** a deeper understanding of the technologies,protocols,and architecture of IoT systems,as well as the potential consequences of using infected IoT devices,is the overarching goal of this *** are many Internet of Things(IoT)systems vulnerable to cybercriminal manipulation,so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.

关键词： cyber defense healthcare devices Internet of Things(IoT) machine learning

来源：评论

学校读者我要写书评

暂无评论

Flying Fox Optimization with Deep Belief Network for Resource Allocation and Offloading Task in IoT Environment 2

Flying Fox Optimization with Deep Belief Network for Resourc...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Jaisharma, K. Al-Fatlawy, Ramy Riad Anu, D. Kumar, K Sudheer Soni, Mukesh Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India The Islamic University College Of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Dehradun India

ISBN: (纸本)9798350372892

In the recent days, mobile edge computing is a booming technology by using the advanced trend technology as Internet of Things (IoT) based devices of mobile. Offloading computation used to maintain the consistence of mobile devices and tasks of application to redirect the data center of cloud system. The mobile edge computing has many regions of devices and access points so in each edge has consists of different resource allocation. Large amount of regions process leads to mobile edge server down. To resolve this issue, we implemented a new approach of flying fox optimization (FFO) with deep learning technique for offloading task and resource allocation. The Deep Belief Network (DBN) used for to find the finest offloading values and optimization algorithm employed for fine tune the parameters of DBN model. Additionally, objective function is defined based on consumption of energy in reducing way. This implemented method (DBN-FFO) enables the finest offloading decision to reduce system cost and significantly works on huge tasks of offloading. Effectiveness of the optimization algorithm tested on single and double models of node. Moreover, it fine-tunes the model parameters in appropriate way. Compared to the other existing methods like deep learning with seagull optimization (DLSGO), Deterministic Policy Gradient (DPG), the proposed DBN-FFO achieved maximum rate of 93.50 %. © 2024 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Compliant

Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Comp...

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Munilla, J. Burmester, M. Caballero-Gil, P. University of Málaga Dpt. of Telecommunication Engineering Málaga29071 Spain Florida State Unviersity Department of Computer Science TallahasseeFL32304 United States University of La Laguna Dept. of Computer Science and Ai Tenerife La Laguna Spain

ISBN: (纸本)9798350364910

Trust and security are critical deployment require-ments for Industrial Internet of Things (IIoT) networks. A recent protocol, called TRUTH, integrates security mechanisms for authentication and privacy alongside a Dempster-Shafer based trust model, to assess the trustworthiness of collected data. We analyze this protocol and show that it does not comply with the implementation requirements of the 5G standard, and that flaws in the identification of involved parties hinder its adoption by 5G devices. We show how to fix this protocol and tailor it for the specific 5G implementation constraints, by enhancing its computational and communication performance. Finally, we show how to prove the security of this fixed protocol (TRUTH +) using the Tamarin automated verification tool. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Robust Audio-Visual ASR with Unified Cross-Modal Attention 48

Robust Audio-Visual ASR with Unified Cross-Modal Attention

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Jiahong Li, Chenda Wu, Yifei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Audio-visual speech recognition (AVSR) takes advantage of noise-invariant visual information to improve the robustness of automatic speech recognition (ASR) systems. While previous works mainly focused on the clean condition, we believe the visual modality is more effective in noisy environments. The challenges arise from the difficulty of adaptive fusion of audio-visual information and the possible interferences inside the training data. In this paper, we present a new audio-visual speech recognition model with a unified cross-modal attention mechanism. In particular, the auxiliary visual evidence is combined with the acoustic feature along the temporal dimension in the unified space before the deep encoding network. This method provides a flexible cross-modal context and requires no forced alignment such that the model can learn to leverage the audio-visual information in relevant frames. In experiments, the proposed model is demonstrated to be robust to the potential absence of the visual modality or misalignment in audio-visual frames. On the large-scale audio-visual dataset LRS3, our new model further reduces the state-of-The-Art WER for clean utterances and significantly improves the performance under noisy conditions. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Diverse and Vivid Sound Generation from Text Descriptions 48

Diverse and Vivid Sound Generation from Text Descriptions

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Guangwei Xu, Xuenan Dai, Lingfeng Wu, Mengyue Yu, Kai Shanghai Jiao Tong University X-Lance Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai China

ISBN: (纸本)9781728163277

Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific audio generation by using natural language description as a clue to generate broad sounds. Unlike visual information, a text description is concise by its nature but has rich hidden meanings beneath, which poses a higher possibility and complexity on the audio to be generated. A Variation-Quantized GAN is used to train a codebook learning discrete representations of spectrograms. For a given text description, its pre-trained embedding is fed to a Transformer to sample codebook indices to decode a spectrogram to be further transformed into waveform by a melgan vocoder. The generated waveform has high quality and fidelity while excellently corresponding to the given text. Experiments show that our proposed method is capable of generating natural, vivid audios, achieving superb quantitative and qualitative results. © 2023 IEEE.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

The New Frontier in Media: ai-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network 7

The New Frontier in Media: AI-Driven Content Creation for Ad...

引用

7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Comcast Software Development and Engineering in Content Discovery and Ai United States Troy University Masters in Computer Science AL. Department of Computer Science Montgomery Campus United States Dt Amtrak Sr. Principal Systems Engineer in C and Rs Platform Services United States University of South Alabama Masters in Electrical Engineering Department of Electrical Engineering United States

ISBN: (纸本)9798331505530

The use of Artificial intelligence (ai) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the ai content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. ai content creation requires more creativity while investigating with a specific goal in mind. Although, ai faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that ai content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. ai content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based ai content creation framework via various metrics. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Exploring Binary Classification Loss for Speaker Verification 48

Exploring Binary Classification Loss for Speaker Verificatio...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Han, Bing Chen, Zhengyang Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric learning-based objectives depend strongly on searching effective pairs which might hinder further improvements. And popular multi-classification methods are usually observed with degradation when evaluated on unseen speakers. In this work, we introduce SphereFace2 framework which uses several binary classifiers to train the speaker model in a pair-wise manner instead of performing multi-classification. Benefiting from this learning paradigm, it can efficiently alleviate the gap between training and evaluation. Experiments conducted on Voxceleb show that the SphereFace2 outperforms other existing loss functions, especially on hard trials. Besides, large margin fine-tuning strategy is proven to be compatible with it for further improvements. Finally, SphereFace2 also shows its strong robustness to class-wise noisy labels which has the potential to be applied in the semi-supervised training scenario with inaccurate estimated pseudo labels. © 2023 IEEE.

关键词： binary classification large margin fine-tuning speaker verification sphereface2

来源：评论

学校读者我要写书评

暂无评论

Optimized Residual Attention U-Net-Based Lung Cancer Detection

Optimized Residual Attention U-Net-Based Lung Cancer Detecti...

引用

International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Jyoti Kumari Sapna Sinha Laxman Singh Department of Information Technology Amity Institute of Information Technology Amity University Noida Uttar Pradesh Department of Computer Science and Engineering (AI-ML) KIET Group of Institutions Ghaziabad Uttar Pradesh

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Lung cancer detection using CT pictures is critical for early detection. Deep learning methods can learn hierarchical representations of image characteristics, but detecting subtle patterns and anomalies is difficult. This study proposed an optimized residual attention U-Net for lung tumor detection using a Gaussian filter and the Binary Grasshopper Optimisation Algorithm (BGOA). The U-Net enhances feature extraction using multi-attention-gated modules and residual layers, broadening the receptive field while avoiding overfitting. The suggested Optimised Residual Attention U-Net increases classification accuracy by 96.66%, the F1-score by 87.98%, precision by 83.53%, AUC by 98.65%, specificity by 97.22%, and recall by 92.94%. This technique overcomes the issues of detecting subtle patterns and anomalies in CT images and improves the outcomes of lung cancer identification.

关键词： Accuracy Computed tomography Lungs Computational modeling Lung cancer Filtering algorithms Feature extraction Classification algorithms Optimization Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Approach for Detecting Liver Tumours in CT images using the Gray Level Co-Occurrence Metrix 1

Machine Learning Approach for Detecting Liver Tumours in CT ...

引用

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICaiSC 2023

作者： Aruna, S. Saranya, A. Guru Pandi, D. Kavya, S.P. Pareek, Piyush Kumar Computational Intelligence SRMIST Chennai India SRMIST Computational Intelligence Department Chennai India Panimalar Engineering College Department of ECE Chennai India KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Arasur India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Over 2.4 % of deaths in India each year are caused by liver diseases. Due to its mild early signs, liver disease is also challenging to diagnose. Frequently, the signs only become obvious when it is too late. Even more challenging than segmenting the liver is segmenting the tumour from the liver. Imaging procedures like computed tomography, magnetic resonance imaging, and ultrasound are utilised to separate the liver and liver tumour once a sample of liver tissue has been removed. This research suggests a machine learning method from CT images-based automatic assistance system for stage categorization. Then, the features are extracted from the CT images using the Gray-Level Co-Occurrence Matrix (GLCM) method. Finally, it is suggested that the computed tomography (CT) pictures of livers containing tumours be categorised using a Random Forest technique. Using the described method, liver tumour images are classified as benign or malignant. The real value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16% based on the experimental investigation. Using the described method, liver tumour images are classified as benign or malignant. These modifications improve the system's ability to recognise the tumour from the CT pictures. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：